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Found 56 Skills
Create data analytics and data pipeline diagrams using PlantUML syntax with analytics/database stencil icons. Best for ETL pipelines, data lakes, real-time streaming, data warehousing, and BI dashboards. NOT for simple flowcharts (use mermaid) or general cloud infra (use cloud skill).
Plan a migration onto MotherDuck. Use when moving from Snowflake, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, validation, rollback, and native-versus-DuckLake posture.
Retrieve Data Lake Object (DLO) and Data Model Object (DMO) schema information from Salesforce Data Cloud using REST APIs. Use this skill when you need to inspect DLO or DMO field definitions, data types, or metadata. Takes org alias and optional DLO/DMO name as parameters.
Core Power BI data modeling, source connectivity, and platform fundamentals. PROACTIVELY activate for: (1) Power BI data modeling and star-schema design, (2) relationships (active/inactive, bidirectional, USERELATIONSHIP), (3) data-source selection (DirectQuery vs Import vs Direct Lake vs composite), (4) incremental refresh setup, (5) gateway configuration (on-prem and VNet gateways), (6) streaming datasets and push-data scenarios, (7) Dataflow Gen2 basics, (8) Power BI common gotchas and pitfalls (bidirectional filtering, AutoExist, blank-row), (9) workspace identity and OAuth2 / service-principal auth, (10) semantic model architecture review. Provides: star-schema templates, mode-selection matrix, incremental refresh recipe, gateway setup steps, and a common-gotchas reference.
Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
Set up end-to-end Change Data Capture (CDC) pipelines on Confluent Cloud using Debezium source connectors, Flink for transformation, and Tableflow for data lake integration. Supports JSON_SR, Avro, and Protobuf formats. Handles schemaless topics (plain JSON without SR) and multi-event topics. This skill handles the complete workflow from database to Iceberg/Delta tables. Use this skill when users want to capture database changes and materialize them into Iceberg or Delta Lake tables via Confluent Cloud Tableflow. Trigger phrases include "CDC to Tableflow", "database to Iceberg", "database to Delta Lake", "stream database changes to data lake", "set up Tableflow pipeline", "schemaless topic to Tableflow", or "multi-event topic to Iceberg". Do NOT trigger for general CDC, Debezium, or database replication requests that do not involve Tableflow or Iceberg/Delta Lake as the destination.
Connect Spice to data sources and query across them with federated SQL. Use when connecting to databases (Postgres, MySQL, DynamoDB), data lakes (S3, Delta Lake, Iceberg), warehouses (Snowflake, Databricks), files, APIs, or catalogs; configuring datasets; creating views; writing data; or setting up cross-source queries.
Retrieves historical PubNub messages via Message Persistence (Storage & Playback). Covers timetoken-based pagination, per-channel ordering guarantees, offline catch-up flows, retention configuration, and the "catch-up tool not a data lake" principle. Use when fetching past messages, paginating with timetokens, building offline-resume UI, retrieving messages with actions, or configuring retention.
Analyze lakehouse data interactively using Fabric Livy sessions and PySpark/Spark SQL for advanced analytics, DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data. Use when the user explicitly asks for PySpark, Spark DataFrames, Livy sessions, or Python-based analysis — NOT for simple SQL queries. Triggers: "PySpark", "Spark SQL", "analyze with PySpark", "Spark DataFrame", "Livy session", "lakehouse with Python", "PySpark analysis", "PySpark data quality", "Delta time-travel with Spark".
Guides agents to discover requirements and design a governed, secure borderless open data lakehouse with agentic AI integration. Use when designing a multi-product architecture that connects data silos to AI agents, joining data across clouds, or running federated queries across Google Cloud and external data sources, including on-premises or other cloud providers. Don't use for simple single-cloud data warehouses or non-AI workloads.
Workload-aware architecture design for Apache Doris. MUST USE when designing data architectures, choosing between data models, planning ingestion strategies, sizing clusters, or translating business requirements into Apache Doris system designs. Complements doris-best-practices with decision frameworks and sizing-first workflow. Use when user describes a workload involving: IoT, sensor data, telemetry, real-time analytics, dashboard, log analysis, log search, CDC sync, time-series, device monitoring, point query service, ad-hoc analytics, lakehouse federation, ETL/ELT pipeline, report analytics, clickstream, user behavior, observability, metrics, fleet tracking, or any OLAP workload requiring table design from scratch. Also triggers on prompts like: "design a table for...", "how should I store...", "build an architecture for...", "we have X devices sending data every Y seconds", "recommend a cluster size for...", "what data model should I use for...", "we need to ingest X GB/day", "migrate from MySQL/PostgreSQL to Apache Doris". Also use for legacy analytics/search/serving stack consolidation prompts even when Apache Doris is not named explicitly, including replacing or migrating from Impala, Kudu, Elasticsearch/ES, Greenplum, Presto, HBase, Hive, Hadoop, Redis, or Lambda-style multi-engine data platforms.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.